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1. The Right Tool for the Job: Data-Centric Workflows in Vizier

   Oliver Kennedy, Boris Glavic (September 2022, Bulletin of the Technical Committee on Data Engineering)

   Sudeepa Roy and Jun Yang (Ed.)

   Data scientists use a wide variety of systems with a wide variety of user interfaces such as spreadsheets and notebooks for their data exploration, discovery, preprocessing, and analysis tasks. While this wide selection of tools offers data scientists the freedom to pick the right tool for each task, each of these tools has limitations (e.g., the lack of reproducibility of notebooks), data needs to be translated between tool-specific formats, and common functionality such as versioning, provenance, and dealing with data errors often has to be implemented for each system. We argue that rather than alternating between task-specific tools, a superior approach is to build multiple user-interfaces on top of a single incremental workflow / dataflow platform with built-in support for versioning, provenance, error & tracking, and data cleaning. We discuss Vizier, a notebook system that implements this approach, introduce the challenges that arose in building such a system, and highlight how our work on Vizier lead to novel research in uncertain data management and incremental execution of workflows. 

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2. vDef-Web: A Case-Study on Building a Science Gateway Around a Research Code

   Dunkel, F.; Bourdin, B.; Brandt, S.R. (September 2019, Gateways 2019)

   Many research codes assume a user’s proficiency with high-performance computing tools, which often hinders their adoption by a community of users. Our goal is to create a user-friendly gateway to allow such users to leverage new ca- pabilities brought forward to the fracture mechanics community by the phase-field approach to fracture, implemented in the open source code vDef. We leveraged popular existing tools for building such frame- works: Agave, Django, and Docker, to build a Science Gateway that allows a user to submit a large number of jobs at once. We use the Agave framework to run jobs and handle all communications with the high-performance computers, as well as data sharing and tracking of provenance. Django was used to create a web application. Docker provided an easily deployable image of the system, simplifying setup by the user. The result is a system that masks all interactions with the high- performance computing environment and provides a graphical interface that makes sense for scientists. In the common situation of parameter sweeps our gateway also helps the scientists comparing outputs of various computations using a matrix view that links to individual computations. 

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3. Heuristic and Cost-based Optimization for Diverse Provenance Tasks

   https://doi.org/10.1109/TKDE.2018.2827074  

   Niu, Xing; Kapoor, Raghav; Glavic, Boris; Gawlick, Dieter; Liu, Zhen Hua; Krishnaswamy, Vasudha; Radhakrishnan, Venkatesh (April 2018, IEEE Transactions on Knowledge and Data Engineering)

   A well-established technique for capturing database provenance as annotations on data is to instrument queries to propagate such annotations. However, even sophisticated query optimizers often fail to produce efficient execution plans for instrumented queries. We develop provenance-aware optimization techniques to address this problem. Specifically, we study algebraic equivalences targeted at instrumented queries and alternative ways of instrumenting queries for provenance capture. Furthermore, we present an extensible heuristic and cost-based optimization framework utilizing these optimizations. Our experiments confirm that these optimizations are highly effective, improving performance by several orders of magnitude for diverse provenance tasks. 

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4. Building Trust in Earth Science Findings through Data Traceability and Results Explainability

   https://doi.org/10.1109/TPDS.2022.3220539  

   Olaya, Paula; Kennedy, Dominic; Llamas, Ricardo; Valera, Leobardo; Vargas, Rodrigo; Lofstead, Jay; Taufer, Michela (January 2022, IEEE Transactions on Parallel and Distributed Systems)

   To trust findings in computational science, scientists need workflows that trace the data provenance and support results explainability. As workflows become more complex, tracing data provenance and explaining results become harder to achieve. In this paper, we propose a computational environment that automatically creates a workflow execution’s record trail and invisibly attaches it to the workflow’s output, enabling data traceability and results explainability. Our solution transforms existing container technology, includes tools for automatically annotating provenance metadata, and allows effective movement of data and metadata across the workflow execution. We demonstrate the capabilities of our environment with the study of SOMOSPIE, an earth science workflow. Through a suite of machine learning modeling techniques, this workflow predicts soil moisture values from the 27 km resolution satellite data down to higher resolutions necessary for policy making and precision agriculture. By running the workflow in our environment, we can identify the causes of different accuracy measurements for predicted soil moisture values in different resolutions of the input data and link different results to different machine learning methods used during the soil moisture downscaling, all without requiring scientists to know aspects of workflow design and implementation. 

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5. Debugging Distributed Systems with Why-Across-Time Provenance

   https://doi.org/10.1145/3267809.3267839  

   Whittaker, Michael; Teodoropol, Cristina; Alvaro, Peter; Hellerstein, Joseph M. (January 2018, Proceedings of the ACM Symposium on Cloud Computing)

   Systematically reasoning about the fine-grained causes of events in a real-world distributed system is challenging. Causality, from the distributed systems literature, can be used to compute the causal history of an arbitrary event in a distributed system, but the event's causal history is an over-approximation of the true causes. Data provenance, from the database literature, precisely describes why a particular tuple appears in the output of a relational query, but data provenance is limited to the domain of static relational databases. In this paper, we present wat-provenance: a novel form of provenance that provides the benefits of causality and data provenance. Given an arbitrary state machine, wat-provenance describes why the state machine produces a particular output when given a particular input. This enables system developers to reason about the causes of events in real-world distributed systems. We observe that automatically extracting the wat-provenance of a state machine is often infeasible. Fortunately, many distributed systems components have simple interfaces from which a developer can directly specify wat-provenance using a technique we call wat-provenance specifications. Leveraging the theoretical foundations of wat-provenance, we implement a prototype distributed debugging framework called Watermelon. 

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